J
Jiyang Zhang
Publications - 6
Citations - 25
Jiyang Zhang is an academic researcher. The author has contributed to research in topics: Computer science & Geology. The author has an hindex of 2, co-authored 6 publications receiving 25 citations.
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Proceedings ArticleDOI
CoditT5: Pretraining for Source Code and Natural Language Editing
TL;DR: A novel pretraining objective is proposed which explicitly models edits and used to build CoditT5, a large language model for software-related editing tasks that is pretrained on large amounts of source code and natural language comments.
Proceedings ArticleDOI
Comparing and Combining Analysis-Based and Learning-Based Regression Test Selection
TL;DR: This work trains several novel ML models to learn the impact of code changes on test outcomes using a training dataset that is obtained via mutation analysis, and evaluates the benefits of combining ML models with analysis-based RTS on 10 projects, compared with using each technique alone.
Journal Article
Using Large-scale Heterogeneous Graph Representation Learning for Code Review Recommendations
Jiyang Zhang,Chandra Maddila,Ramakrishna Bairi,Christian Bird,Ujjwal Raizada,Apoorva Agrawal,Yamini Jhawar,Kim Herzig,Arie van Deursen +8 more
TL;DR: Coral is presented, a novel approach to reviewer recommendation that leverages a sociotechnical graph built from the rich set of entities and their relationships in modern source code management systems and is able to model the manual history of reviewer selection remarkably well.
Proceedings ArticleDOI
Python-by-contract dataset
TL;DR: The Python-by-contract dataset as discussed by the authors contains 514 Python functions annotated with contracts using icontract library and can be easily used by existing testing tools that take advantage of contracts.
Proceedings ArticleDOI
More Precise Regression Test Selection via Reasoning about Semantics-Modifying Changes
TL;DR: In this paper , the authors propose to use program analysis to trade precision for speed, or even use machine learning to trade safety for speed to further speed up regression testing, and evaluate the impact on safety and precision of leveraging such changes.